def test_original_biased_nonlin_semi_nmf(self): auv = sio.loadmat(mat_file) u, v = auv['u'], auv['v'] a = relu(u @ v) bias_v = np.vstack((v, np.ones((1, v.shape[1])))) old_loss = np_frobenius_norm(a, u @ v) a_ph = tf.placeholder(tf.float64, shape=a.shape) u_ph = tf.placeholder(tf.float64, shape=u.shape) bias_v_ph = tf.placeholder(tf.float64, shape=bias_v.shape) tf_bias_u, tf_v = nonlin_semi_nmf(a_ph, u_ph, bias_v_ph, use_bias=True, use_tf=True, num_calc_v=0) init = tf.global_variables_initializer() with tf.Session() as sess: init.run() start_time = time.time() _u, _bias_v = sess.run([tf_bias_u, tf_v], feed_dict={a_ph: a, u_ph: u, bias_v_ph: bias_v}) end_time = time.time() duration = end_time - start_time _bias_u = np.hstack((_u, np.ones((_u.shape[0], 1)))) new_loss = np_frobenius_norm(a, relu(_bias_u @ _bias_v)) assert a.shape == (_bias_u @ _bias_v).shape assert new_loss < old_loss, "new loss should be less than old loss." print_format('TensorFlow', 'biased Nonlinear semi-NMF(NOT CALC v)', a, _bias_u, _bias_v, old_loss, new_loss, duration)
def test_np_not_calc_v_biased_nonlin_semi_nmf(self): auv = sio.loadmat(mat_file) a, u, v = auv['a'], auv['u'], auv['v'] old_loss = np_frobenius_norm(a, u @ v) biased_u = np.hstack((u, np.ones((u.shape[0], 1)))) start_time = time.time() biased_u, v = nonlin_semi_nmf(a, biased_u, v, use_bias=True, num_calc_v=0) end_time = time.time() duration = end_time - start_time bias_v = np.vstack((v, np.ones((1, v.shape[1])))) new_loss = np_frobenius_norm(a, relu(biased_u @ bias_v)) assert a.shape == (biased_u @ bias_v).shape assert new_loss < old_loss, "new loss should be less than old loss." print('\n[Numpy]Solve biased Nonlinear semi-NMF(NOT CALCULATE v)\n\t' 'old loss {0}\n\t' 'new loss {1}\n\t' 'process duration {2}'.format(old_loss, new_loss, duration))
def test_tf_not_calc_v_nonlin_semi_nmf(self): auv = sio.loadmat(mat_file) a, u, v = auv['a'], auv['u'], auv['v'] old_loss = np_frobenius_norm(a, u @ v) # [1000, 500] a_ph = tf.placeholder(tf.float64, shape=a.shape) # [1000, 201] u_ph = tf.placeholder(tf.float64, shape=u.shape) # [200, 500] v_ph = tf.placeholder(tf.float64, shape=v.shape) tf_u, tf_v = nonlin_semi_nmf(a_ph, u_ph, v_ph, use_tf=True, use_bias=False, num_calc_v=0, num_calc_u=1) tf_loss = frobenius_norm(a_ph, tf.nn.relu(tf.matmul(tf_u, tf_v))) init = tf.global_variables_initializer() with tf.Session() as sess: init.run() start_time = time.time() _u, _v, new_loss = sess.run([tf_u, tf_v, tf_loss], feed_dict={a_ph: a, u_ph: u, v_ph: v}) end_time = time.time() duration = end_time - start_time assert a.shape == (_u @ _v).shape assert new_loss < old_loss, "new loss should be less than old loss." print_format('TensorFlow', 'Nonlinear semi-NMF(NOT CALCLATE v)', a, u, v, old_loss, new_loss, duration)
def _autoencoder(self): updates = [] layers = self._layers[:-1] for i, layer in enumerate(layers): a = layer.output # [3000, 784] u = self._layers[i + 1].output kernel = layer.kernel temporary_shape = utility.transpose_shape(kernel) # [1000, 784] if layer.use_bias: temporary_shape[0] += 1 kernel = tf.concat((kernel, layer.bias[None, ...]), axis=0) temporary_kernel = tf.get_variable( 'temporal_{}'.format(i), temporary_shape, dtype=tf.float64, initializer=tf.contrib.layers.xavier_initializer(), trainable=False) u, _ = mf.semi_nmf( a=a, u=u, v=temporary_kernel, use_tf=True, use_bias=layer.use_bias, num_iters=1, first_nneg=True, ) # Not use activation (ReLU) if not layer.activation: _, v = mf.semi_nmf( a=u, u=a, v=kernel, use_tf=True, use_bias=layer.use_bias, num_iters=1, first_nneg=True, ) # Use activation (ReLU) # else utility.get_op_name(layer.activation) == 'Relu': else: _, v = mf.nonlin_semi_nmf( a=u, u=a, v=kernel, use_tf=True, use_bias=layer.use_bias, num_calc_v=0, num_calc_u=1, first_nneg=True, ) if layer.use_bias: v, bias = utility.split_v_bias(v) updates.append(layer.bias.assign(bias)) updates.append(layer.kernel.assign(v)) return tf.group(*updates)
def minimize(self, loss=None): """Construct the control dependencies for calculating neural net optimized. Returns: tf.no_op. The import """ self._init(loss) if self._use_autoencoder: self._autoencoder() a = self.labels updates = [] # Reverse layers = self._layers[::-1] for i, layer in enumerate(layers): _u = layer.output v = layer.kernel # Check whether u is a tensor or not. # that is Recurrent output if it have dim more than 3. if _u.shape.ndims >= 3 and not isinstance(layer.recurrent, tf.Variable): u = _u[:, -1, :] else: u = _u if isinstance(layer.recurrent, tf.Variable): v = tf.concat((layer.kernel, layer.recurrent), axis=0) if layer.use_bias: v = tf.concat((v, layer.bias[None, ...]), axis=0) # Not use activation (ReLU) if not layer.activation: u, v = mf.semi_nmf(a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, num_iters=1, first_nneg=True, ) # Use activation (ReLU) else: u, v = mf.nonlin_semi_nmf(a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, num_calc_v=1, num_calc_u=1, first_nneg=True, ) if layer.use_bias: v, bias = utility.split_v_bias(v) updates.append(layer.bias.assign(bias)) updates.append(layer.kernel.assign(v)) a = tf.identity(_u) return tf.group(*updates)
def minimize(self, loss=None): """Construct the control dependencies for calculating neural net optimized. Returns: tf.no_op. The import """ self._init(loss) if self._use_autoencoder: self._autoencoder() a = self.labels updates = [] # Reverse layers = self._layers[::-1] for i, layer in enumerate(layers): u = layer.output v = layer.kernel if layer.use_bias: v = tf.concat((v, layer.bias[None, ...]), axis=0) # Not use activation (ReLU) if not layer.activation: u, v = mf.semi_nmf( a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, num_iters=1, first_nneg=True, ) # Use activation (ReLU) else: u, v = mf.nonlin_semi_nmf( a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, num_calc_v=1, num_calc_u=1, first_nneg=True, ) if layer.use_bias: v, bias = utility.split_v_bias(v) updates.append(layer.bias.assign(bias)) updates.append(layer.kernel.assign(v)) a = tf.identity(u) return tf.group(*updates)
def test_np_not_calc_v_vanilla_nonlin_semi_nmf(self): a = np.random.uniform(0., 1., size=(100, 100)) u = np.random.uniform(0., 1., size=(100, 300)) v = np.random.uniform(-1., 1., size=(300, 100)) old_loss = np_frobenius_norm(a, u @ v) start_time = time.time() u, v = nonlin_semi_nmf(a, u, v, use_bias=False, num_calc_v=0) assert np.min(u) > 0, np.min(u) end_time = time.time() duration = end_time - start_time new_loss = np_frobenius_norm(a, relu(u @ v)) assert a.shape == (u @ v).shape assert new_loss < old_loss, "new loss should be less than old loss." print_format('Numpy', 'Nonlinear semi-NMF(NOT CALCULATE v)', a, u, v, old_loss, new_loss, duration)
def test_np_not_calc_v_vanilla_nonlin_semi_nmf(self): auv = sio.loadmat(mat_file) a, u, v = auv['a'], auv['u'], auv['v'] old_loss = np_frobenius_norm(a, u @ v) start_time = time.time() u, v = nonlin_semi_nmf(a, u, v, use_bias=False, num_calc_v=0) end_time = time.time() duration = end_time - start_time new_loss = np_frobenius_norm(a, relu(u @ v)) assert a.shape == (u @ v).shape assert new_loss < old_loss, "new loss should be less than old loss." print('\n[Numpy]Solve Nonlinear semi-NMF(NOT CALCULATE v)\n\t' 'old loss {0}\n\t' 'new loss {1}\n\t' 'process duration {2}'.format(old_loss, new_loss, duration))
def test_np_not_calc_v_biased_nonlin_semi_nmf(self): a = np.random.uniform(0., 1., size=(100, 100)) u = np.random.uniform(0., 1., size=(100, 300)) v = np.random.uniform(-1., 1., size=(300, 100)) old_loss = np_frobenius_norm(a, u @ v) bias_v = np.vstack((v, np.ones((1, v.shape[1])))) start_time = time.time() u, bias_v = nonlin_semi_nmf(a, u, bias_v, use_bias=True, num_calc_v=0) assert np.min(u) > 0, np.min(u) end_time = time.time() duration = end_time - start_time bias_u = np.hstack((u, np.ones((u.shape[0], 1)))) new_loss = np_frobenius_norm(a, relu(bias_u @ bias_v)) assert a.shape == (bias_u @ bias_v).shape assert new_loss < old_loss, "new loss should be less than old loss." print_format('Numpy', 'biased Nonlinear semi-NMF(NOT CALC v)', a, bias_u, bias_v, old_loss, new_loss, duration)
def test_tf_not_calc_v_biased_nonlin_semi_nmf(self): auv = sio.loadmat(mat_file) a, u, v = auv['a'], auv['u'], auv['v'] bias_u = np.hstack((u, np.ones((u.shape[0], 1)))) old_loss = np_frobenius_norm(a, u @ v) a_ph = tf.placeholder(tf.float64, shape=a.shape) bias_u_ph = tf.placeholder(tf.float64, shape=bias_u.shape) v_ph = tf.placeholder(tf.float64, shape=v.shape) tf_bias_u, tf_v = nonlin_semi_nmf(a_ph, bias_u_ph, v_ph, num_calc_v=0, use_bias=True, use_tf=True) init = tf.global_variables_initializer() with tf.Session() as sess: init.run() start_time = time.time() _bias_u, _v = sess.run([tf_bias_u, tf_v], feed_dict={ a_ph: a, bias_u_ph: bias_u, v_ph: v }) end_time = time.time() duration = end_time - start_time _bias_v = np.vstack((_v, np.ones((1, v.shape[1])))) new_loss = np_frobenius_norm(a, relu(_bias_u @ _bias_v)) assert a.shape == (_bias_u @ _bias_v).shape assert new_loss < old_loss, "new loss should be less than old loss." print( '\n[TensorFlow]Solve biased Nonlinear semi-NMF(NOT CALCULATE v)\n\t' 'old loss {0}\n\t' 'new loss {1}\n\t' 'process duration {2}'.format(old_loss, new_loss, duration))
def minimize(self, loss=None, pretrain=False): """Construct the control dependencies for calculating neural net optimized. Returns: tf.no_op. The import """ self._init(loss) # pre-train with auto encoder. pretrain_op = self._autoencoder() if pretrain else tf.no_op() a = self.labels updates = [] # Reverse layers = self._layers[::-1] for i, layer in enumerate(layers): u = layer.output v = layer.kernel if layer.use_bias: v = tf.concat((v, layer.bias[None, ...]), axis=0) # Not use activation (ReLU) if not layer.activation: u, v = mf.semi_nmf( a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, num_iters=1, first_nneg=True, ) # Use activation (ReLU) elif utility.get_op_name(layer.activation) == 'Relu': u, v = mf.nonlin_semi_nmf( a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, num_calc_v=1, num_calc_u=1, first_nneg=True, ) # Use Softmax elif utility.get_op_name(layer.activation) == 'Softmax': print('used softmax!!') u, v = mf.softmax_nmf( a=a, u=u, v=v, use_tf=True, use_bias=layer.use_bias, ) if layer.use_bias: v, bias = utility.split_v_bias(v) updates.append(layer.bias.assign(bias)) updates.append(layer.kernel.assign(v)) a = tf.identity(u) return AttrDict(ae=pretrain_op, nmf=tf.group(*updates))